Federated Learning is a machine learning technique that trains an algorithm across multiple decentralized edge devices, without the need to bring the data to a central location. In this approach, the data remains on the local devices, and the updates to the algorithm iteratively occur on the same devices. Federated learning can be useful in scenarios where the data is distributed across multiple devices or is sensitive to transfer. This technique is commonly used in areas such as healthcare, where patient data privacy is a top priority.
Federated Learning allows access to a wide variety of data sets without needing to directly share sensitive data, whether it’s between a user and a server or between organizations.
To begin, each edge device learns an initial model from local data that gets sent to the server. From there, the various user-specific models are averaged at the central server to come up with an updated global model and complete what is known as a Federated Learning Round. This process can then be repeated as required to come up with improved versions of the model.
Federated Learning comes with several benefits that include enhanced privacy, greatly reduced learning time, reduced cost of training, and enhanced regulatory compliance.
Unfortunately, there are also some drawbacks – including debate over whether it provides privacy benefits in the first place. With Federated Learning, it is possible to reverse engineer the underlying data sets based on metadata revealed by the model once it’s complete, and the model is known by all collaborating parties. That said there are platforms such as Duality platform that address those concerns by providing a secured federated learning.
